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Expressing in language is subjective. Everyone has a different style ofreading and writing, apparently it all boil downs to the way their mindunderstands things (in a specific format). Language style transfer is a way topreserve the meaning of a text and change the way it is expressed. Progress inlanguage style transfer is lagged behind other domains, such as computervision, mainly because of the lack of parallel data, use cases, and reliableevaluation metrics. In response to the challenge of lacking parallel data, weexplore learning style transfer from non-parallel data. We propose a modelcombining seq2seq, autoencoders, and adversarial loss to achieve this goal. Thekey idea behind the proposed models is to learn separate contentrepresentations and style representations using adversarial networks.Considering the problem of evaluating style transfer tasks, we frame theproblem as sentiment transfer and evaluation using a sentiment classifier tocalculate how many sentiments was the model able to transfer. We report ourresults on several kinds of models.

Sentiment Transfer using Seq2Seq Adversarial Autoencoders